Research and Realization of Grouping Collection Algorithm of Infrared Cage Heating Circuit for Spacecraft Vacuum Thermal Test

Abstract

In the process of spacecraft thermal test, the infrared cage is mainly used to simulate the heat flow outside the space. In the design, the infrared cage is divided into several heating zones corresponding to the spacecraft. The heating strip of the infrared cage is divided into heating circuits, the heating circuits are grouped and collected, and the infrared cage heating node table is defined as an important basis for controlling the infrared cage. At present, there is a single method of grouping and gathering heating circuits, which is mostly limited by manual adjustment based on experience and lack of automation. In order to solve the shortcomings of the existing technology, this paper adopts the genetic algorithm hybrid BFD based on reinforcement learning to group and collect the heating circuits, and optimize the design results according to the constraints. The user interaction interface is designed to facilitate the user to do further analysis. Thereby improving the level of business automation, optimizing working procedures and reducing costs.